
"""simple docstring"""



import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionAttendAndExcitePipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


a       :						Dict       		=		False




@skip_mps
class 		a_    (			_UpperCAmelCase  ,    _UpperCAmelCase  ,    _UpperCAmelCase  ,    unittest.TestCase						):
    a   :					List[str]			     =					StableDiffusionAttendAndExcitePipeline
    a   :					Optional[int]			     =					False
    a   :					Tuple			     =					TEXT_TO_IMAGE_PARAMS
    a   :					Union[str, Any]			     =					TEXT_TO_IMAGE_BATCH_PARAMS.union({'token_indices'}						)
    a   :					Tuple			     =					TEXT_TO_IMAGE_IMAGE_PARAMS
    a   :					List[str]			     =					TEXT_TO_IMAGE_IMAGE_PARAMS







    @classmethod
    def   _snake_case       (      cls      :							Optional[int]  )		->Union[str, Any]:






        '''simple docstring'''






        super().setUpClass()
        torch.use_deterministic_algorithms(__UpperCamelCase  )







    @classmethod
    def   _snake_case       (      cls      :							Union[str, Any]  )		->Tuple:






        '''simple docstring'''






        super().tearDownClass()
        torch.use_deterministic_algorithms(__UpperCamelCase  )







    def   _snake_case       (      self      :							int  )		->List[str]:






        '''simple docstring'''






        torch.manual_seed(0  )
        _UpperCAmelCase        = UNetaDConditionModel(
            block_out_channels=(32, 64)  ,       layers_per_block=1  ,       sample_size=32  ,       in_channels=4  ,       out_channels=4  ,       down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")  ,       up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")  ,       cross_attention_dim=32  ,       attention_head_dim=(2, 4)  ,       use_linear_projection=__UpperCamelCase  ,       )
        _UpperCAmelCase        = DDIMScheduler(
            beta_start=0.0_0_0_8_5  ,       beta_end=0.0_1_2  ,       beta_schedule="""scaled_linear"""  ,       clip_sample=__UpperCamelCase  ,       set_alpha_to_one=__UpperCamelCase  ,       )
        torch.manual_seed(0  )
        _UpperCAmelCase        = AutoencoderKL(
            block_out_channels=[32, 64]  ,       in_channels=3  ,       out_channels=3  ,       down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]  ,       up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]  ,       latent_channels=4  ,       sample_size=1_28  ,       )
        torch.manual_seed(0  )
        _UpperCAmelCase        = CLIPTextConfig(
            bos_token_id=0  ,       eos_token_id=2  ,       hidden_size=32  ,       intermediate_size=37  ,       layer_norm_eps=1e-05  ,       num_attention_heads=4  ,       num_hidden_layers=5  ,       pad_token_id=1  ,       vocab_size=10_00  ,       hidden_act="""gelu"""  ,       projection_dim=5_12  ,       )
        _UpperCAmelCase        = CLIPTextModel(__UpperCamelCase  )
        _UpperCAmelCase        = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""  )

        _UpperCAmelCase        = {
            """unet""": unet,
            """scheduler""": scheduler,
            """vae""": vae,
            """text_encoder""": text_encoder,
            """tokenizer""": tokenizer,
            """safety_checker""": None,
            """feature_extractor""": None,
        }

        return components







    def   _snake_case       (      self      :							List[str]  ,       __UpperCamelCase      :							Dict  ,       __UpperCamelCase      :							Dict=0  )		->Union[str, Any]:






        '''simple docstring'''






        if str(__UpperCamelCase  ).startswith("""mps"""  ):
            _UpperCAmelCase        = torch.manual_seed(__UpperCamelCase  )
        else:
            _UpperCAmelCase        = torch.Generator(device=__UpperCamelCase  ).manual_seed(__UpperCamelCase  )
        _UpperCAmelCase        = _UpperCAmelCase        = {
            """prompt""": """a cat and a frog""",
            """token_indices""": [2, 5],
            """generator""": generator,
            """num_inference_steps""": 1,
            """guidance_scale""": 6.0,
            """output_type""": """numpy""",
            """max_iter_to_alter""": 2,
            """thresholds""": {0: 0.7},
        }
        return inputs







    def   _snake_case       (      self      :							Dict  )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase        = """cpu"""

        _UpperCAmelCase        = self.get_dummy_components()
        _UpperCAmelCase        = self.pipeline_class(**__UpperCamelCase  )
        pipe.to(__UpperCamelCase  )
        pipe.set_progress_bar_config(disable=__UpperCamelCase  )

        _UpperCAmelCase        = self.get_dummy_inputs(__UpperCamelCase  )
        _UpperCAmelCase        = pipe(**__UpperCamelCase  ).images
        _UpperCAmelCase        = image[0, -3:, -3:, -1]

        self.assertEqual(image.shape  ,       (1, 64, 64, 3)  )
        _UpperCAmelCase        = np.array(
            [0.6_3_9_0_5_3_6_4, 0.6_2_8_9_7_3_0_7, 0.4_8_5_9_9_0_1_7, 0.5_1_3_3_6_2_4, 0.5_5_5_0_0_4_8, 0.4_5_7_6_9_5_1_6, 0.5_0_3_2_6_9_7_3, 0.5_0_2_3_1_3_9, 0.4_5_3_8_4_4_9_6]  )
        _UpperCAmelCase        = np.abs(image_slice.flatten() - expected_slice  ).max()
        self.assertLessEqual(__UpperCamelCase  ,       1e-3  )







    def   _snake_case       (      self      :							Any  )		->str:






        '''simple docstring'''






        super().test_cpu_offload_forward_pass(expected_max_diff=5e-4  )







    def   _snake_case       (      self      :							Optional[int]  )		->int:






        '''simple docstring'''






        self._test_inference_batch_consistent(batch_sizes=[1, 2]  )







    def   _snake_case       (      self      :							Any  )		->List[Any]:






        '''simple docstring'''






        self._test_inference_batch_single_identical(batch_size=2  ,       expected_max_diff=7e-4  )







    def   _snake_case       (      self      :							Tuple  )		->Optional[int]:






        '''simple docstring'''






        super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3  )







    def   _snake_case       (      self      :							Dict  )		->Dict:






        '''simple docstring'''






        super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5e-4  )







    def   _snake_case       (      self      :							Tuple  )		->Tuple:






        '''simple docstring'''






        super().test_save_load_local(expected_max_difference=5e-4  )







    def   _snake_case       (      self      :							str  )		->str:






        '''simple docstring'''






        super().test_save_load_optional_components(expected_max_difference=4e-4  )







@require_torch_gpu
@slow
class 		a_    (			unittest.TestCase						):







    @classmethod
    def   _snake_case       (      cls      :							str  )		->Dict:






        '''simple docstring'''






        super().setUpClass()
        torch.use_deterministic_algorithms(__UpperCamelCase  )







    @classmethod
    def   _snake_case       (      cls      :							Optional[int]  )		->str:






        '''simple docstring'''






        super().tearDownClass()
        torch.use_deterministic_algorithms(__UpperCamelCase  )







    def   _snake_case       (      self      :							Optional[Any]  )		->int:






        '''simple docstring'''






        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







    def   _snake_case       (      self      :							int  )		->int:






        '''simple docstring'''






        _UpperCAmelCase        = torch.manual_seed(51  )

        _UpperCAmelCase        = StableDiffusionAttendAndExcitePipeline.from_pretrained(
            """CompVis/stable-diffusion-v1-4"""  ,       safety_checker=__UpperCamelCase  ,       torch_dtype=torch.floataa  )
        pipe.to("""cuda"""  )

        _UpperCAmelCase        = """a painting of an elephant with glasses"""
        _UpperCAmelCase        = [5, 7]

        _UpperCAmelCase        = pipe(
            prompt=__UpperCamelCase  ,       token_indices=__UpperCamelCase  ,       guidance_scale=7.5  ,       generator=__UpperCamelCase  ,       num_inference_steps=5  ,       max_iter_to_alter=5  ,       output_type="""numpy"""  ,       ).images[0]

        _UpperCAmelCase        = load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy"""  )
        assert np.abs((expected_image - image).max()  ) < 5e-1

"""simple docstring"""



import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter

import requests


def 			_UpperCamelCase						(    _A       ,   _A=None   )     ->	Optional[int]:





    """simple docstring"""

    _UpperCAmelCase        = None
    if token is not None:
        _UpperCAmelCase        = {"""Accept""": """application/vnd.github+json""", """Authorization""": F"""Bearer {token}"""}

    _UpperCAmelCase        = F"""https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"""
    _UpperCAmelCase        = requests.get(_A       ,   headers=_A   ).json()
    _UpperCAmelCase        = {}

    try:
        job_links.update({job["""name"""]: job["""html_url"""] for job in result["""jobs"""]}   )
        _UpperCAmelCase        = math.ceil((result["""total_count"""] - 1_0_0) / 1_0_0   )

        for i in range(_A   ):
            _UpperCAmelCase        = requests.get(url + F"""&page={i + 2}"""       ,   headers=_A   ).json()
            job_links.update({job["""name"""]: job["""html_url"""] for job in result["""jobs"""]}   )

        return job_links
    except Exception:
        print(F"""Unknown error, could not fetch links:\n{traceback.format_exc()}"""   )

    return {}


def 			_UpperCamelCase						(    _A       ,   _A=None   )     ->	str:





    """simple docstring"""

    _UpperCAmelCase        = None
    if token is not None:
        _UpperCAmelCase        = {"""Accept""": """application/vnd.github+json""", """Authorization""": F"""Bearer {token}"""}

    _UpperCAmelCase        = F"""https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100"""
    _UpperCAmelCase        = requests.get(_A       ,   headers=_A   ).json()
    _UpperCAmelCase        = {}

    try:
        artifacts.update({artifact["""name"""]: artifact["""archive_download_url"""] for artifact in result["""artifacts"""]}   )
        _UpperCAmelCase        = math.ceil((result["""total_count"""] - 1_0_0) / 1_0_0   )

        for i in range(_A   ):
            _UpperCAmelCase        = requests.get(url + F"""&page={i + 2}"""       ,   headers=_A   ).json()
            artifacts.update({artifact["""name"""]: artifact["""archive_download_url"""] for artifact in result["""artifacts"""]}   )

        return artifacts
    except Exception:
        print(F"""Unknown error, could not fetch links:\n{traceback.format_exc()}"""   )

    return {}


def 			_UpperCamelCase						(    _A       ,   _A       ,   _A       ,   _A   )     ->	List[Any]:





    """simple docstring"""

    _UpperCAmelCase        = None
    if token is not None:
        _UpperCAmelCase        = {"""Accept""": """application/vnd.github+json""", """Authorization""": F"""Bearer {token}"""}

    _UpperCAmelCase        = requests.get(_A       ,   headers=_A       ,   allow_redirects=_A   )
    _UpperCAmelCase        = result.headers["""Location"""]
    _UpperCAmelCase        = requests.get(_A       ,   allow_redirects=_A   )
    _UpperCAmelCase        = os.path.join(_A       ,   F"""{artifact_name}.zip"""   )
    with open(_A       ,   """wb"""   ) as fp:
        fp.write(response.content   )


def 			_UpperCamelCase						(    _A       ,   _A=None   )     ->	Union[str, Any]:





    """simple docstring"""

    _UpperCAmelCase        = []
    _UpperCAmelCase        = []
    _UpperCAmelCase        = None

    with zipfile.ZipFile(_A   ) as z:
        for filename in z.namelist():
            if not os.path.isdir(_A   ):
                # read the file
                if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
                    with z.open(_A   ) as f:
                        for line in f:
                            _UpperCAmelCase        = line.decode("""UTF-8"""   ).strip()
                            if filename == "failures_line.txt":
                                try:
                                    # `error_line` is the place where `error` occurs
                                    _UpperCAmelCase        = line[: line.index(""": """   )]
                                    _UpperCAmelCase        = line[line.index(""": """   ) + len(""": """   ) :]
                                    errors.append([error_line, error]   )
                                except Exception:
                                    # skip un-related lines
                                    pass
                            elif filename == "summary_short.txt" and line.startswith("""FAILED """   ):
                                # `test` is the test method that failed
                                _UpperCAmelCase        = line[len("""FAILED """   ) :]
                                failed_tests.append(_A   )
                            elif filename == "job_name.txt":
                                _UpperCAmelCase        = line

    if len(_A   ) != len(_A   ):
        raise ValueError(
            F"""`errors` and `failed_tests` should have the same number of elements. Got {len(_A   )} for `errors` """
            F"""and {len(_A   )} for `failed_tests` instead. The test reports in {artifact_zip_path} have some"""
            """ problem."""   )

    _UpperCAmelCase        = None
    if job_name and job_links:
        _UpperCAmelCase        = job_links.get(_A       ,   _A   )

    # A list with elements of the form (line of error, error, failed test)
    _UpperCAmelCase        = [x + [y] + [job_link] for x, y in zip(_A       ,   _A   )]

    return result


def 			_UpperCamelCase						(    _A       ,   _A=None   )     ->	List[str]:





    """simple docstring"""

    _UpperCAmelCase        = []

    _UpperCAmelCase        = [os.path.join(_A       ,   _A   ) for p in os.listdir(_A   ) if p.endswith(""".zip"""   )]
    for p in paths:
        errors.extend(get_errors_from_single_artifact(_A       ,   job_links=_A   )   )

    return errors


def 			_UpperCamelCase						(    _A       ,   _A=None   )     ->	Union[str, Any]:





    """simple docstring"""

    _UpperCAmelCase        = Counter()
    counter.update([x[1] for x in logs]   )
    _UpperCAmelCase        = counter.most_common()
    _UpperCAmelCase        = {}
    for error, count in counts:
        if error_filter is None or error not in error_filter:
            _UpperCAmelCase        = {"""count""": count, """failed_tests""": [(x[2], x[0]) for x in logs if x[1] == error]}

    _UpperCAmelCase        = dict(sorted(r.items()       ,   key=lambda _A   : item[1]["count"]       ,   reverse=_A   )   )
    return r


def 			_UpperCamelCase						(    _A   )     ->	int:





    """simple docstring"""

    _UpperCAmelCase        = test.split("""::"""   )[0]
    if test.startswith("""tests/models/"""   ):
        _UpperCAmelCase        = test.split("""/"""   )[2]
    else:
        _UpperCAmelCase        = None

    return test


def 			_UpperCamelCase						(    _A       ,   _A=None   )     ->	str:





    """simple docstring"""

    _UpperCAmelCase        = [(x[0], x[1], get_model(x[2]   )) for x in logs]
    _UpperCAmelCase        = [x for x in logs if x[2] is not None]
    _UpperCAmelCase        = {x[2] for x in logs}

    _UpperCAmelCase        = {}
    for test in tests:
        _UpperCAmelCase        = Counter()
        # count by errors in `test`
        counter.update([x[1] for x in logs if x[2] == test]   )
        _UpperCAmelCase        = counter.most_common()
        _UpperCAmelCase        = {error: count for error, count in counts if (error_filter is None or error not in error_filter)}
        _UpperCAmelCase        = sum(error_counts.values()   )
        if n_errors > 0:
            _UpperCAmelCase        = {"""count""": n_errors, """errors""": error_counts}

    _UpperCAmelCase        = dict(sorted(r.items()       ,   key=lambda _A   : item[1]["count"]       ,   reverse=_A   )   )
    return r


def 			_UpperCamelCase						(    _A   )     ->	Optional[int]:





    """simple docstring"""

    _UpperCAmelCase        = """| no. | error | status |"""
    _UpperCAmelCase        = """|-:|:-|:-|"""
    _UpperCAmelCase        = [header, sep]
    for error in reduced_by_error:
        _UpperCAmelCase        = reduced_by_error[error]["""count"""]
        _UpperCAmelCase        = F"""| {count} | {error[:1_0_0]} |  |"""
        lines.append(_A   )

    return "\n".join(_A   )


def 			_UpperCamelCase						(    _A   )     ->	int:





    """simple docstring"""

    _UpperCAmelCase        = """| model | no. of errors | major error | count |"""
    _UpperCAmelCase        = """|-:|-:|-:|-:|"""
    _UpperCAmelCase        = [header, sep]
    for model in reduced_by_model:
        _UpperCAmelCase        = reduced_by_model[model]["""count"""]
        _UpperCAmelCase     ,_UpperCAmelCase        = list(reduced_by_model[model]["""errors"""].items()   )[0]
        _UpperCAmelCase        = F"""| {model} | {count} | {error[:6_0]} | {_count} |"""
        lines.append(_A   )

    return "\n".join(_A   )


if __name__ == "__main__":
 a       :						Optional[Any]       		=		argparse.ArgumentParser()
 # Required parameters
 parser.add_argument('''--workflow_run_id''', type=str, required=True, help='''A GitHub Actions workflow run id.''')
 parser.add_argument(
     '''--output_dir''',
     type=str,
     required=True,
     help='''Where to store the downloaded artifacts and other result files.''',
 )
 parser.add_argument('''--token''', default=None, type=str, help='''A token that has actions:read permission.''')
 a       :						Any       		=		parser.parse_args()

 os.makedirs(args.output_dir, exist_ok=True)

 a       :						Optional[int]       		=		get_job_links(args.workflow_run_id, token=args.token)
 a       :						Optional[int]       		=		{}
 # To deal with `workflow_call` event, where a job name is the combination of the job names in the caller and callee.
 # For example, `PyTorch 1.11 / Model tests (models/albert, single-gpu)`.
 if _job_links:
  for k, v in _job_links.items():
   # This is how GitHub actions combine job names.
   if " / " in k:
    a       :						Union[str, Any]       		=		k.find(''' / ''')
    a       :						Optional[int]       		=		k[index + len(''' / ''') :]
   a       :						Optional[int]       		=		v
 with open(os.path.join(args.output_dir, '''job_links.json'''), '''w''', encoding='''UTF-8''') as fp:
  json.dump(job_links, fp, ensure_ascii=False, indent=4)

 a       :						Union[str, Any]       		=		get_artifacts_links(args.workflow_run_id, token=args.token)
 with open(os.path.join(args.output_dir, '''artifacts.json'''), '''w''', encoding='''UTF-8''') as fp:
  json.dump(artifacts, fp, ensure_ascii=False, indent=4)

 for idx, (name, url) in enumerate(artifacts.items()):
  download_artifact(name, url, args.output_dir, args.token)
  # Be gentle to GitHub
  time.sleep(1)

 a       :						Any       		=		get_all_errors(args.output_dir, job_links=job_links)

 # `e[1]` is the error
 a       :						int       		=		Counter()
 counter.update([e[1] for e in errors])

 # print the top 30 most common test errors
 a       :						List[str]       		=		counter.most_common(3_0)
 for item in most_common:
  print(item)

 with open(os.path.join(args.output_dir, '''errors.json'''), '''w''', encoding='''UTF-8''') as fp:
  json.dump(errors, fp, ensure_ascii=False, indent=4)

 a       :						List[Any]       		=		reduce_by_error(errors)
 a       :						List[str]       		=		reduce_by_model(errors)

 a       :						Dict       		=		make_github_table(reduced_by_error)
 a       :						int       		=		make_github_table_per_model(reduced_by_model)

 with open(os.path.join(args.output_dir, '''reduced_by_error.txt'''), '''w''', encoding='''UTF-8''') as fp:
  fp.write(sa)
 with open(os.path.join(args.output_dir, '''reduced_by_model.txt'''), '''w''', encoding='''UTF-8''') as fp:
  fp.write(sa)